from sklearn_benchmarks.report import Reporting
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
reporting = Reporting(config_file_path="config.yml")
reporting.run()
| hour | min | sec | |
|---|---|---|---|
| algo | |||
| KNeighborsClassifier | 0.0 | 34.0 | 3.778037 |
| daal4py_KNeighborsClassifier | 0.0 | 2.0 | 23.643444 |
| KNeighborsClassifier_kd_tree | 0.0 | 2.0 | 32.052489 |
| daal4py_KNeighborsClassifier_kd_tree | 0.0 | 0.0 | 26.916706 |
| KMeans_tall | 0.0 | 0.0 | 22.365274 |
| daal4py_KMeans_tall | 0.0 | 0.0 | 8.406904 |
| KMeans_short | 0.0 | 0.0 | 2.710550 |
| daal4py_KMeans_short | 0.0 | 0.0 | 1.364891 |
| LogisticRegression | 0.0 | 0.0 | 19.761589 |
| daal4py_LogisticRegression | 0.0 | 0.0 | 4.238672 |
| Ridge | 0.0 | 0.0 | 11.438993 |
| daal4py_Ridge | 0.0 | 0.0 | 1.998984 |
| HistGradientBoostingClassifier | 0.0 | 5.0 | 8.080488 |
| lightgbm | 0.0 | 5.0 | 25.801415 |
| xgboost | 0.0 | 5.0 | 42.813769 |
| catboost | 0.0 | 5.0 | 4.885658 |
| total | 1.0 | 2.0 | 0.341927 |
All estimators share the following hyperparameters:
| value | |
|---|---|
| algorithm | brute |
| estimator | function | n_samples_train | n_samples | n_features | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | n_jobs | n_neighbors | accuracy_score_sklearn | accuracy_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.284 | 0.000 | 2.818 | 0.000 | 1 | 100 | NaN | NaN | 0.492 | 0.000 | 0.576 | 0.000 | See | See |
| 1 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 24.278 | 0.238 | 0.000 | 0.024 | 1 | 100 | 0.928 | 0.938 | 1.751 | 0.009 | 13.864 | 0.152 | See | See |
| 2 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.212 | 0.001 | 0.000 | 0.212 | 1 | 100 | 1.000 | 1.000 | 0.090 | 0.001 | 2.354 | 0.030 | See | See |
| 3 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.137 | 0.000 | 5.826 | 0.000 | 1 | 1 | NaN | NaN | 0.483 | 0.000 | 0.284 | 0.000 | See | See |
| 4 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 13.697 | 0.058 | 0.000 | 0.014 | 1 | 1 | 0.698 | 0.825 | 1.689 | 0.008 | 8.111 | 0.050 | See | See |
| 5 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.201 | 0.001 | 0.000 | 0.201 | 1 | 1 | 1.000 | 0.000 | 0.090 | 0.002 | 2.236 | 0.040 | See | See |
| 6 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.139 | 0.000 | 5.768 | 0.000 | 1 | 5 | NaN | NaN | 0.482 | 0.000 | 0.288 | 0.000 | See | See |
| 7 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 23.876 | 0.054 | 0.000 | 0.024 | 1 | 5 | 0.782 | 0.704 | 1.689 | 0.009 | 14.134 | 0.081 | See | See |
| 8 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.212 | 0.001 | 0.000 | 0.212 | 1 | 5 | 1.000 | 0.000 | 0.093 | 0.009 | 2.274 | 0.210 | See | See |
| 9 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.139 | 0.000 | 5.755 | 0.000 | -1 | 1 | NaN | NaN | 0.483 | 0.000 | 0.288 | 0.000 | See | See |
| 10 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 25.175 | 0.149 | 0.000 | 0.025 | -1 | 1 | 0.698 | 0.825 | 1.686 | 0.005 | 14.928 | 0.100 | See | See |
| 11 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.175 | 0.013 | 0.000 | 0.175 | -1 | 1 | 1.000 | 0.000 | 0.090 | 0.001 | 1.939 | 0.146 | See | See |
| 12 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.141 | 0.000 | 5.678 | 0.000 | -1 | 5 | NaN | NaN | 0.484 | 0.000 | 0.291 | 0.000 | See | See |
| 13 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 35.457 | 0.000 | 0.000 | 0.035 | -1 | 5 | 0.782 | 0.938 | 1.748 | 0.008 | 20.284 | 0.091 | See | See |
| 14 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.179 | 0.014 | 0.000 | 0.179 | -1 | 5 | 1.000 | 1.000 | 0.090 | 0.001 | 1.998 | 0.157 | See | See |
| 15 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.132 | 0.000 | 6.070 | 0.000 | -1 | 100 | NaN | NaN | 0.482 | 0.000 | 0.273 | 0.000 | See | See |
| 16 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 35.530 | 0.000 | 0.000 | 0.036 | -1 | 100 | 0.928 | 0.704 | 1.694 | 0.016 | 20.975 | 0.200 | See | See |
| 17 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.187 | 0.011 | 0.000 | 0.187 | -1 | 100 | 1.000 | 0.000 | 0.094 | 0.010 | 1.991 | 0.240 | See | See |
| 18 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.054 | 0.000 | 0.297 | 0.000 | 1 | 100 | NaN | NaN | 0.100 | 0.000 | 0.537 | 0.000 | See | See |
| 19 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 19.234 | 0.019 | 0.000 | 0.019 | 1 | 100 | 0.980 | 0.970 | 0.301 | 0.002 | 63.917 | 0.388 | See | See |
| 20 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.026 | 0.001 | 0.000 | 0.026 | 1 | 100 | 1.000 | 1.000 | 0.005 | 0.001 | 4.982 | 0.567 | See | See |
| 21 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.053 | 0.000 | 0.305 | 0.000 | 1 | 1 | NaN | NaN | 0.100 | 0.000 | 0.523 | 0.000 | See | See |
| 22 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 10.720 | 0.006 | 0.000 | 0.011 | 1 | 1 | 0.975 | 0.969 | 0.254 | 0.000 | 42.174 | 0.048 | See | See |
| 23 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.013 | 0.001 | 0.000 | 0.013 | 1 | 1 | 1.000 | 1.000 | 0.005 | 0.001 | 2.636 | 0.347 | See | See |
| 24 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.052 | 0.000 | 0.305 | 0.000 | 1 | 5 | NaN | NaN | 0.100 | 0.000 | 0.524 | 0.000 | See | See |
| 25 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 19.205 | 0.012 | 0.000 | 0.019 | 1 | 5 | 0.979 | 0.960 | 0.254 | 0.002 | 75.549 | 0.658 | See | See |
| 26 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.026 | 0.001 | 0.000 | 0.026 | 1 | 5 | 1.000 | 1.000 | 0.005 | 0.000 | 5.083 | 0.511 | See | See |
| 27 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.053 | 0.000 | 0.304 | 0.000 | -1 | 1 | NaN | NaN | 0.101 | 0.000 | 0.522 | 0.000 | See | See |
| 28 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 22.114 | 0.095 | 0.000 | 0.022 | -1 | 1 | 0.975 | 0.969 | 0.255 | 0.001 | 86.799 | 0.425 | See | See |
| 29 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.020 | 0.003 | 0.000 | 0.020 | -1 | 1 | 1.000 | 1.000 | 0.005 | 0.000 | 4.015 | 0.704 | See | See |
| 30 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.053 | 0.000 | 0.301 | 0.000 | -1 | 5 | NaN | NaN | 0.100 | 0.000 | 0.530 | 0.000 | See | See |
| 31 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 30.618 | 0.000 | 0.000 | 0.031 | -1 | 5 | 0.979 | 0.970 | 0.301 | 0.002 | 101.623 | 0.685 | See | See |
| 32 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.032 | 0.001 | 0.000 | 0.032 | -1 | 5 | 1.000 | 1.000 | 0.005 | 0.001 | 6.091 | 0.654 | See | See |
| 33 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.052 | 0.000 | 0.305 | 0.000 | -1 | 100 | NaN | NaN | 0.101 | 0.000 | 0.520 | 0.000 | See | See |
| 34 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 30.574 | 0.000 | 0.000 | 0.031 | -1 | 100 | 0.980 | 0.960 | 0.253 | 0.000 | 120.855 | 0.178 | See | See |
| 35 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.034 | 0.003 | 0.000 | 0.034 | -1 | 100 | 1.000 | 1.000 | 0.005 | 0.000 | 6.611 | 0.820 | See | See |
All estimators share the following hyperparameters:
| value | |
|---|---|
| algorithm | kd_tree |
| estimator | function | n_samples_train | n_samples | n_features | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | n_jobs | n_neighbors | accuracy_score_sklearn | accuracy_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 2.732 | 0.000 | 0.029 | 0.000 | 1 | 100 | NaN | NaN | 0.681 | 0.000 | 4.012 | 0.000 | See | See |
| 1 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 4.635 | 0.016 | 0.000 | 0.005 | 1 | 100 | 0.977 | 0.961 | 0.099 | 0.001 | 46.969 | 0.406 | See | See |
| 2 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.004 | 0.001 | 0.000 | 0.004 | 1 | 100 | 1.000 | 1.000 | 0.000 | 0.000 | 12.340 | 6.581 | See | See |
| 3 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 2.740 | 0.000 | 0.029 | 0.000 | -1 | 100 | NaN | NaN | 0.668 | 0.000 | 4.102 | 0.000 | See | See |
| 4 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 2.657 | 0.008 | 0.000 | 0.003 | -1 | 100 | 0.977 | 0.977 | 0.181 | 0.003 | 14.705 | 0.244 | See | See |
| 5 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.005 | 0.001 | 0.000 | 0.005 | -1 | 100 | 1.000 | 1.000 | 0.000 | 0.000 | 14.318 | 6.766 | See | See |
| 6 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 2.775 | 0.000 | 0.029 | 0.000 | -1 | 1 | NaN | NaN | 0.670 | 0.000 | 4.145 | 0.000 | See | See |
| 7 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 0.425 | 0.003 | 0.000 | 0.000 | -1 | 1 | 0.956 | 0.961 | 0.104 | 0.012 | 4.102 | 0.478 | See | See |
| 8 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.003 | 0.000 | 0.000 | 0.003 | -1 | 1 | 1.000 | 1.000 | 0.000 | 0.000 | 8.520 | 4.158 | See | See |
| 9 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 2.748 | 0.000 | 0.029 | 0.000 | -1 | 5 | NaN | NaN | 0.673 | 0.000 | 4.086 | 0.000 | See | See |
| 10 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 0.794 | 0.004 | 0.000 | 0.001 | -1 | 5 | 0.980 | 0.977 | 0.180 | 0.002 | 4.403 | 0.055 | See | See |
| 11 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.003 | 0.000 | 0.000 | 0.003 | -1 | 5 | 1.000 | 1.000 | 0.000 | 0.000 | 7.256 | 3.266 | See | See |
| 12 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 2.752 | 0.000 | 0.029 | 0.000 | 1 | 1 | NaN | NaN | 0.665 | 0.000 | 4.141 | 0.000 | See | See |
| 13 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 0.710 | 0.003 | 0.000 | 0.001 | 1 | 1 | 0.956 | 0.972 | 0.536 | 0.002 | 1.324 | 0.008 | See | See |
| 14 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 1 | 1.000 | 1.000 | 0.001 | 0.000 | 1.321 | 0.637 | See | See |
| 15 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 2.743 | 0.000 | 0.029 | 0.000 | 1 | 5 | NaN | NaN | 0.666 | 0.000 | 4.120 | 0.000 | See | See |
| 16 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 1.364 | 0.009 | 0.000 | 0.001 | 1 | 5 | 0.980 | 0.972 | 0.536 | 0.004 | 2.547 | 0.024 | See | See |
| 17 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 5 | 1.000 | 1.000 | 0.001 | 0.000 | 1.592 | 0.748 | See | See |
| 18 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.729 | 0.000 | 0.022 | 0.000 | 1 | 100 | NaN | NaN | 0.431 | 0.000 | 1.691 | 0.000 | See | See |
| 19 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.045 | 0.003 | 0.000 | 0.000 | 1 | 100 | 0.979 | 0.968 | 0.001 | 0.000 | 60.241 | 19.136 | See | See |
| 20 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 100 | 1.000 | 1.000 | 0.000 | 0.000 | 6.332 | 4.599 | See | See |
| 21 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.728 | 0.000 | 0.022 | 0.000 | -1 | 100 | NaN | NaN | 0.435 | 0.000 | 1.673 | 0.000 | See | See |
| 22 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.041 | 0.002 | 0.000 | 0.000 | -1 | 100 | 0.979 | 0.982 | 0.001 | 0.000 | 36.393 | 10.505 | See | See |
| 23 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.002 | 0.000 | 0.000 | 0.002 | -1 | 100 | 1.000 | 1.000 | 0.000 | 0.000 | 20.134 | 15.371 | See | See |
| 24 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.733 | 0.000 | 0.022 | 0.000 | -1 | 1 | NaN | NaN | 0.428 | 0.000 | 1.713 | 0.000 | See | See |
| 25 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.024 | 0.002 | 0.001 | 0.000 | -1 | 1 | 0.966 | 0.968 | 0.001 | 0.000 | 33.746 | 10.677 | See | See |
| 26 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.002 | 0.000 | 0.000 | 0.002 | -1 | 1 | 1.000 | 1.000 | 0.000 | 0.000 | 20.246 | 15.970 | See | See |
| 27 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.727 | 0.000 | 0.022 | 0.000 | -1 | 5 | NaN | NaN | 0.427 | 0.000 | 1.702 | 0.000 | See | See |
| 28 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.025 | 0.001 | 0.001 | 0.000 | -1 | 5 | 0.977 | 0.982 | 0.001 | 0.000 | 24.446 | 6.523 | See | See |
| 29 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.002 | 0.000 | 0.000 | 0.002 | -1 | 5 | 1.000 | 1.000 | 0.000 | 0.000 | 20.706 | 16.259 | See | See |
| 30 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.727 | 0.000 | 0.022 | 0.000 | 1 | 1 | NaN | NaN | 0.430 | 0.000 | 1.690 | 0.000 | See | See |
| 31 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.022 | 0.001 | 0.001 | 0.000 | 1 | 1 | 0.966 | 0.983 | 0.006 | 0.001 | 3.549 | 0.470 | See | See |
| 32 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 1 | 1.000 | 1.000 | 0.000 | 0.000 | 5.342 | 4.063 | See | See |
| 33 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.727 | 0.000 | 0.022 | 0.000 | 1 | 5 | NaN | NaN | 0.432 | 0.000 | 1.683 | 0.000 | See | See |
| 34 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.024 | 0.001 | 0.001 | 0.000 | 1 | 5 | 0.977 | 0.983 | 0.006 | 0.001 | 3.809 | 0.565 | See | See |
| 35 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 5 | 1.000 | 1.000 | 0.000 | 0.000 | 4.984 | 3.558 | See | See |
All estimators share the following hyperparameters:
| value | |
|---|---|
| algorithm | full |
| n_clusters | 3 |
| max_iter | 30 |
| n_init | 1 |
| tol | 0.0 |
| estimator | function | n_samples_train | n_samples | n_features | n_iter_sklearn | mean_sklearn | stdev_sklearn | throughput | latency | init | adjusted_rand_score_sklearn | n_iter_daal4py | adjusted_rand_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KMeans_tall | fit | 1000000 | 1000000 | 2 | 30 | 0.573 | 0.0 | 0.837 | 0.000 | random | NaN | 30 | NaN | 0.371 | 0.0 | 1.545 | 0.000 | See | See |
| 1 | KMeans_tall | predict | 1000000 | 1000 | 2 | 30 | 0.001 | 0.0 | 0.376 | 0.000 | random | 0.001 | 30 | 0.001 | 0.000 | 0.0 | 9.194 | 6.356 | See | See |
| 2 | KMeans_tall | predict | 1000000 | 1 | 2 | 30 | 0.001 | 0.0 | 0.000 | 0.001 | random | 1.000 | 30 | 1.000 | 0.000 | 0.0 | 10.510 | 7.136 | See | See |
| 3 | KMeans_tall | fit | 1000000 | 1000000 | 2 | 30 | 0.550 | 0.0 | 0.873 | 0.000 | k-means++ | NaN | 30 | NaN | 0.404 | 0.0 | 1.360 | 0.000 | See | See |
| 4 | KMeans_tall | predict | 1000000 | 1000 | 2 | 30 | 0.001 | 0.0 | 0.385 | 0.000 | k-means++ | 0.001 | 30 | 0.000 | 0.000 | 0.0 | 8.914 | 5.774 | See | See |
| 5 | KMeans_tall | predict | 1000000 | 1 | 2 | 30 | 0.001 | 0.0 | 0.000 | 0.001 | k-means++ | 1.000 | 30 | 1.000 | 0.000 | 0.0 | 10.717 | 8.031 | See | See |
| 6 | KMeans_tall | fit | 1000000 | 1000000 | 100 | 30 | 6.252 | 0.0 | 3.839 | 0.000 | random | NaN | 30 | NaN | 2.890 | 0.0 | 2.164 | 0.000 | See | See |
| 7 | KMeans_tall | predict | 1000000 | 1000 | 100 | 30 | 0.002 | 0.0 | 15.727 | 0.000 | random | 0.002 | 30 | 0.002 | 0.000 | 0.0 | 5.974 | 2.640 | See | See |
| 8 | KMeans_tall | predict | 1000000 | 1 | 100 | 30 | 0.001 | 0.0 | 0.020 | 0.001 | random | 1.000 | 30 | 1.000 | 0.000 | 0.0 | 10.234 | 6.915 | See | See |
| 9 | KMeans_tall | fit | 1000000 | 1000000 | 100 | 30 | 6.242 | 0.0 | 3.845 | 0.000 | k-means++ | NaN | 30 | NaN | 3.064 | 0.0 | 2.037 | 0.000 | See | See |
| 10 | KMeans_tall | predict | 1000000 | 1000 | 100 | 30 | 0.002 | 0.0 | 15.631 | 0.000 | k-means++ | 0.002 | 30 | 0.002 | 0.000 | 0.0 | 5.786 | 2.883 | See | See |
| 11 | KMeans_tall | predict | 1000000 | 1 | 100 | 30 | 0.001 | 0.0 | 0.020 | 0.001 | k-means++ | 1.000 | 30 | 1.000 | 0.000 | 0.0 | 10.337 | 7.200 | See | See |
All estimators share the following hyperparameters:
| value | |
|---|---|
| algorithm | full |
| n_clusters | 300 |
| max_iter | 20 |
| n_init | 1 |
| tol | 0.0 |
| estimator | function | n_samples_train | n_samples | n_features | n_iter_sklearn | mean_sklearn | stdev_sklearn | throughput | latency | init | adjusted_rand_score_sklearn | n_iter_daal4py | adjusted_rand_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KMeans_short | fit | 10000 | 10000 | 2 | 20 | 0.072 | 0.000 | 0.044 | 0.000 | random | NaN | 20 | NaN | 0.091 | 0.0 | 0.791 | 0.000 | See | See |
| 1 | KMeans_short | predict | 10000 | 1000 | 2 | 20 | 0.002 | 0.000 | 0.193 | 0.000 | random | 0.001 | 20 | 0.001 | 0.001 | 0.0 | 2.661 | 0.436 | See | See |
| 2 | KMeans_short | predict | 10000 | 1 | 2 | 20 | 0.001 | 0.000 | 0.000 | 0.001 | random | 1.000 | 20 | 1.000 | 0.000 | 0.0 | 9.113 | 5.543 | See | See |
| 3 | KMeans_short | fit | 10000 | 10000 | 2 | 20 | 0.217 | 0.000 | 0.015 | 0.000 | k-means++ | NaN | 20 | NaN | 0.028 | 0.0 | 7.673 | 0.000 | See | See |
| 4 | KMeans_short | predict | 10000 | 1000 | 2 | 20 | 0.002 | 0.000 | 0.192 | 0.000 | k-means++ | 0.000 | 20 | 0.002 | 0.001 | 0.0 | 2.657 | 0.576 | See | See |
| 5 | KMeans_short | predict | 10000 | 1 | 2 | 20 | 0.001 | 0.000 | 0.000 | 0.001 | k-means++ | 1.000 | 20 | 1.000 | 0.000 | 0.0 | 9.814 | 6.710 | See | See |
| 6 | KMeans_short | fit | 10000 | 10000 | 100 | 20 | 0.188 | 0.000 | 0.851 | 0.000 | random | NaN | 20 | NaN | 0.309 | 0.0 | 0.608 | 0.000 | See | See |
| 7 | KMeans_short | predict | 10000 | 1000 | 100 | 20 | 0.003 | 0.001 | 6.185 | 0.000 | random | 0.301 | 20 | 0.301 | 0.001 | 0.0 | 2.366 | 0.636 | See | See |
| 8 | KMeans_short | predict | 10000 | 1 | 100 | 20 | 0.001 | 0.000 | 0.012 | 0.001 | random | 1.000 | 20 | 1.000 | 0.000 | 0.0 | 7.550 | 3.604 | See | See |
| 9 | KMeans_short | fit | 10000 | 10000 | 100 | 20 | 0.576 | 0.000 | 0.278 | 0.000 | k-means++ | NaN | 20 | NaN | 0.124 | 0.0 | 4.654 | 0.000 | See | See |
| 10 | KMeans_short | predict | 10000 | 1000 | 100 | 20 | 0.002 | 0.000 | 6.745 | 0.000 | k-means++ | 0.300 | 20 | 0.325 | 0.001 | 0.0 | 2.172 | 0.386 | See | See |
| 11 | KMeans_short | predict | 10000 | 1 | 100 | 20 | 0.001 | 0.000 | 0.012 | 0.001 | k-means++ | 1.000 | 20 | 1.000 | 0.000 | 0.0 | 8.120 | 4.537 | See | See |
All estimators share the following hyperparameters:
| value | |
|---|---|
| penalty | l2 |
| dual | False |
| tol | 0.0001 |
| C | 1.0 |
| fit_intercept | True |
| intercept_scaling | 1 |
| class_weight | NaN |
| random_state | NaN |
| solver | lbfgs |
| max_iter | 100 |
| multi_class | auto |
| verbose | 0 |
| warm_start | False |
| n_jobs | NaN |
| l1_ratio | NaN |
| estimator | function | n_samples_train | n_samples | n_features | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | class_weight | l1_ratio | n_jobs | random_state | accuracy_score | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | LogisticRegression | fit | 1000000 | 1000000 | 100 | [20] | 10.950 | 0.0 | [-0.10774664] | 0.000 | NaN | NaN | NaN | NaN | NaN | 1.998 | 0.0 | 5.481 | 0.000 | See | See |
| 1 | LogisticRegression | predict | 1000000 | 1000 | 100 | [20] | 0.000 | 0.0 | [52.69703421] | 0.000 | NaN | NaN | NaN | NaN | 0.56 | 0.000 | 0.0 | 0.887 | 0.456 | See | See |
| 2 | LogisticRegression | predict | 1000000 | 1 | 100 | [20] | 0.000 | 0.0 | [0.2552628] | 0.000 | NaN | NaN | NaN | NaN | 1.00 | 0.000 | 0.0 | 0.345 | 0.324 | See | See |
| 3 | LogisticRegression | fit | 1000 | 1000 | 10000 | [27] | 0.808 | 0.0 | [-2.64338962] | 0.001 | NaN | NaN | NaN | NaN | NaN | 0.830 | 0.0 | 0.974 | 0.000 | See | See |
| 4 | LogisticRegression | predict | 1000 | 100 | 10000 | [27] | 0.002 | 0.0 | [134.92220048] | 0.000 | NaN | NaN | NaN | NaN | 0.29 | 0.003 | 0.0 | 0.552 | 0.127 | See | See |
| 5 | LogisticRegression | predict | 1000 | 1 | 10000 | [27] | 0.000 | 0.0 | [26.03645587] | 0.000 | NaN | NaN | NaN | NaN | 0.00 | 0.001 | 0.0 | 0.118 | 0.091 | See | See |
All estimators share the following hyperparameters:
| value | |
|---|---|
| alpha | 1.0 |
| fit_intercept | True |
| normalize | deprecated |
| copy_X | True |
| max_iter | NaN |
| tol | 0.001 |
| solver | auto |
| random_state | NaN |
| estimator | function | n_samples_train | n_samples | n_features | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | max_iter | random_state | r2_score | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Ridge | fit | 1000 | 1000 | 10000 | NaN | 0.179 | 0.000 | 0.447 | 0.0 | NaN | NaN | NaN | 0.184 | 0.000 | 0.975 | 0.000 | See | See |
| 1 | Ridge | predict | 1000 | 1000 | 10000 | NaN | 0.011 | 0.001 | 6.974 | 0.0 | NaN | NaN | 0.109 | 0.019 | 0.001 | 0.606 | 0.037 | See | See |
| 2 | Ridge | predict | 1000 | 1 | 10000 | NaN | 0.000 | 0.000 | 1.250 | 0.0 | NaN | NaN | NaN | 0.000 | 0.000 | 0.642 | 0.654 | See | See |
| 3 | Ridge | fit | 1000000 | 1000000 | 100 | NaN | 1.433 | 0.000 | 0.558 | 0.0 | NaN | NaN | NaN | 0.247 | 0.000 | 5.796 | 0.000 | See | See |
| 4 | Ridge | predict | 1000000 | 1000 | 100 | NaN | 0.000 | 0.000 | 5.609 | 0.0 | NaN | NaN | 1.000 | 0.000 | 0.000 | 0.623 | 0.434 | See | See |
| 5 | Ridge | predict | 1000000 | 1 | 100 | NaN | 0.000 | 0.000 | 0.015 | 0.0 | NaN | NaN | NaN | 0.000 | 0.000 | 0.607 | 0.687 | See | See |
{
"system_info": {
"python": "3.8.10 | packaged by conda-forge | (default, May 11 2021, 07:01:05) [GCC 9.3.0]",
"executable": "/usr/share/miniconda/envs/sklbench/bin/python",
"machine": "Linux-5.4.0-1047-azure-x86_64-with-glibc2.10"
},
"dependencies_info": {
"pip": "21.1.2",
"setuptools": "49.6.0.post20210108",
"sklearn": "1.0.dev0",
"numpy": "1.20.3",
"scipy": "1.6.3",
"Cython": null,
"pandas": "1.2.4",
"matplotlib": "3.4.2",
"joblib": "1.0.1",
"threadpoolctl": "2.1.0"
},
"threadpool_info": [
{
"filepath": "/usr/share/miniconda/envs/sklbench/lib/libopenblasp-r0.3.15.so",
"prefix": "libopenblas",
"user_api": "blas",
"internal_api": "openblas",
"version": "0.3.15",
"num_threads": 2,
"threading_layer": "pthreads"
},
{
"filepath": "/usr/share/miniconda/envs/sklbench/lib/python3.8/site-packages/scikit_learn.libs/libgomp-f7e03b3e.so.1.0.0",
"prefix": "libgomp",
"user_api": "openmp",
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"version": null,
"num_threads": 2
},
{
"filepath": "/usr/share/miniconda/envs/sklbench/lib/libgomp.so.1.0.0",
"prefix": "libgomp",
"user_api": "openmp",
"internal_api": "openmp",
"version": null,
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}
],
"cpu_count": 2
}